IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v67y2021i11p6734-6750.html
   My bibliography  Save this article

Seeding the Herd: Pricing and Welfare Effects of Social Learning Manipulation

Author

Listed:
  • Li Chen

    (Samuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, New York 14853)

  • Yiangos Papanastasiou

    (Haas School of Business, University of California, Berkeley, California 94720)

Abstract

This paper is motivated by the recent emergence of various interference tactics employed by sellers attempting to manipulate social learning. We revisit the classic model of observational social learning and extend it to allow for (i) asymmetric information on product value between the seller and the consumers and (ii) the ability of the seller to “seed” the observational learning process with a fake purchase, in an attempt to manipulate consumer beliefs. We examine the interaction between social learning manipulation and equilibrium market outcomes as well as the impact of antimanipulation measures aimed at detecting and punishing misconduct. The analysis yields three main insights. First, we show that increasing the intensity of antimanipulation measures can have unintended consequences, often inducing higher levels of manipulation as well as higher equilibrium prices. Second, we find that although measures of high intensity can completely deter misconduct, such measures do not lead to any improvement in either seller or consumer payoffs, relative to the case where no measures are present. Third, we demonstrate that in many cases, measures of intermediate intensity can leverage seller manipulation to simultaneously improve both seller and consumer payoffs.

Suggested Citation

  • Li Chen & Yiangos Papanastasiou, 2021. "Seeding the Herd: Pricing and Welfare Effects of Social Learning Manipulation," Management Science, INFORMS, vol. 67(11), pages 6734-6750, November.
    • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:11:p:6734-6750
    DOI: 10.1287/mnsc.2020.3849
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.2020.3849
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.2020.3849?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Dina Mayzlin & Yaniv Dover & Judith Chevalier, 2014. "Promotional Reviews: An Empirical Investigation of Online Review Manipulation," American Economic Review, American Economic Association, vol. 104(8), pages 2421-2455, August.
    2. Yeon-Koo Che & Johannes Hörner, 2018. "Recommender Systems as Mechanisms for Social Learning," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 871-925.
    3. Hubert Pun & Gregory D. DeYong, 2017. "Competing with Copycats When Customers Are Strategic," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 403-418, July.
    4. Senthil K. Veeraraghavan & Laurens G. Debo, 2011. "Herding in Queues with Waiting Costs: Rationality and Regret," Manufacturing & Service Operations Management, INFORMS, vol. 13(3), pages 329-346, July.
    5. Herrera, Helios & Hörner, Johannes, 2013. "Biased social learning," Games and Economic Behavior, Elsevier, vol. 80(C), pages 131-146.
    6. Davide Crapis & Bar Ifrach & Costis Maglaras & Marco Scarsini, 2017. "Monopoly Pricing in the Presence of Social Learning," Management Science, INFORMS, vol. 63(11), pages 3586-3608, November.
    7. Senthil Veeraraghavan & Laurens Debo, 2009. "Joining Longer Queues: Information Externalities in Queue Choice," Manufacturing & Service Operations Management, INFORMS, vol. 11(4), pages 543-562, April.
    8. Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2011. "Bayesian Learning in Social Networks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(4), pages 1201-1236.
    9. Yiangos Papanastasiou & Kostas Bimpikis & Nicos Savva, 2018. "Crowdsourcing Exploration," Management Science, INFORMS, vol. 64(4), pages 1727-1746, April.
    10. In-Koo Cho & David M. Kreps, 1987. "Signaling Games and Stable Equilibria," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 102(2), pages 179-221.
    11. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    12. David Godes, 2017. "Product Policy in Markets with Word-of-Mouth Communication," Management Science, INFORMS, vol. 63(1), pages 267-278, January.
    13. Guarino, Antonio & Harmgart, Heike & Huck, Steffen, 2011. "Aggregate information cascades," Games and Economic Behavior, Elsevier, vol. 73(1), pages 167-185, September.
    14. Pnina Feldman & Yiangos Papanastasiou & Ella Segev, 2019. "Social Learning and the Design of New Experience Goods," Management Science, INFORMS, vol. 65(5), pages 1502-1519, April.
    15. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    16. Lones Smith & Peter Sorensen, 2000. "Pathological Outcomes of Observational Learning," Econometrica, Econometric Society, vol. 68(2), pages 371-398, March.
    17. Laurens Debo & Uday Rajan & Senthil K. Veeraraghavan, 2020. "Signaling Quality via Long Lines and Uninformative Prices," Manufacturing & Service Operations Management, INFORMS, vol. 22(3), pages 513-527, May.
    18. Soo-Haeng Cho & Xin Fang & Sridhar Tayur, 2015. "Combating Strategic Counterfeiters in Licit and Illicit Supply Chains," Manufacturing & Service Operations Management, INFORMS, vol. 17(3), pages 273-289, July.
    19. Ying-Ju Chen & Tinglong Dai & C. Gizem Korpeoglu & Ersin Körpeoğlu & Ozge Sahin & Christopher S. Tang & Shihong Xiao, 2020. "OM Forum—Innovative Online Platforms: Research Opportunities," Manufacturing & Service Operations Management, INFORMS, vol. 22(3), pages 430-445, May.
    20. Laurens G. Debo & Christine Parlour & Uday Rajan, 2012. "Signaling Quality via Queues," Management Science, INFORMS, vol. 58(5), pages 876-891, May.
    21. Michael Luca & Georgios Zervas, 2016. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Management Science, INFORMS, vol. 62(12), pages 3412-3427, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohamed Mostagir & James Siderius, 2023. "Strategic Reviews," Management Science, INFORMS, vol. 69(2), pages 904-921, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Davide Crapis & Bar Ifrach & Costis Maglaras & Marco Scarsini, 2017. "Monopoly Pricing in the Presence of Social Learning," Management Science, INFORMS, vol. 63(11), pages 3586-3608, November.
    2. Parakhonyak, Alexei & Vikander, Nick, 2023. "Information design through scarcity and social learning," Journal of Economic Theory, Elsevier, vol. 207(C).
    3. Bar Ifrach & Costis Maglaras & Marco Scarsini & Anna Zseleva, 2019. "Bayesian Social Learning from Consumer Reviews," Operations Research, INFORMS, vol. 67(5), pages 1209-1221, September.
    4. Eyster, Erik & Galeotti, Andrea & Kartik, Navin & Rabin, Matthew, 2014. "Congested observational learning," Games and Economic Behavior, Elsevier, vol. 87(C), pages 519-538.
    5. Monzón, Ignacio & Rapp, Michael, 2014. "Observational learning with position uncertainty," Journal of Economic Theory, Elsevier, vol. 154(C), pages 375-402.
    6. Pnina Feldman & Yiangos Papanastasiou & Ella Segev, 2019. "Social Learning and the Design of New Experience Goods," Management Science, INFORMS, vol. 65(5), pages 1502-1519, April.
    7. Liangfei Qiu & Arunima Chhikara & Asoo Vakharia, 2021. "Multidimensional Observational Learning in Social Networks: Theory and Experimental Evidence," Information Systems Research, INFORMS, vol. 32(3), pages 876-894, September.
    8. Yiangos Papanastasiou, 2020. "Fake News Propagation and Detection: A Sequential Model," Management Science, INFORMS, vol. 66(5), pages 1826-1846, May.
    9. Cripps, Martin W. & Thomas, Caroline D., 2019. "Strategic experimentation in queues," Theoretical Economics, Econometric Society, vol. 14(2), May.
    10. Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asuman Ozdaglar, 2017. "Fast and Slow Learning From Reviews," NBER Working Papers 24046, National Bureau of Economic Research, Inc.
    11. Ilai Bistritz & Nasimeh Heydaribeni & Achilleas Anastasopoulos, 2019. "Do Informational Cascades Happen with Non-myopic Agents?," Papers 1905.01327, arXiv.org, revised Jul 2022.
    12. Can Küçükgül & Özalp Özer & Shouqiang Wang, 2022. "Engineering Social Learning: Information Design of Time-Locked Sales Campaigns for Online Platforms," Management Science, INFORMS, vol. 68(7), pages 4899-4918, July.
    13. James C. D. Fisher & John Wooders, 2017. "Interacting information cascades: on the movement of conventions between groups," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 63(1), pages 211-231, January.
    14. Laurens G. Debo & Christine Parlour & Uday Rajan, 2012. "Signaling Quality via Queues," Management Science, INFORMS, vol. 58(5), pages 876-891, May.
    15. Tommaso Bondi, 2019. "Alone, Together. Product Discovery Through Consumer Ratings," Working Papers 19-09, NET Institute.
    16. Deepanshu Vasal & Achilleas Anastasopoulos, 2016. "Decentralized Bayesian learning in dynamic games: A framework for studying informational cascades," Papers 1607.06847, arXiv.org, revised Apr 2018.
    17. Kimon Drakopoulos & Ali Makhdoumi, 2023. "Providing Data Samples for Free," Management Science, INFORMS, vol. 69(6), pages 3536-3560, June.
    18. Bingsheng Liu & Wenwen Zhu & Yinghua Shen & Yuan Chen & Tao Wang & Fengwen Chen & Maggie Wenjing Liu & Shi‐Hao Zhou, 2022. "A study about return policies in the presence of consumer social learning," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2571-2587, June.
    19. Andrew M. Davis & Vishal Gaur & Dayoung Kim, 2021. "Consumer Learning from Own Experience and Social Information: An Experimental Study," Management Science, INFORMS, vol. 67(5), pages 2924-2943, May.
    20. Yiangos Papanastasiou & Kostas Bimpikis & Nicos Savva, 2018. "Crowdsourcing Exploration," Management Science, INFORMS, vol. 64(4), pages 1727-1746, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:67:y:2021:i:11:p:6734-6750. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.